A cortical locus for modulation of arousal states
Bibliographic record
Abstract
Fluctuations in global arousal are key determinants of spontaneous cortical activity and function. Several subcortical structures, including neuromodulator nuclei like the locus coeruleus (LC), are involved in the regulation of arousal. However, much less is known about the role of cortical circuits that provide top-down inputs to arousal-related subcortical structures. Here, we investigated the role of a major subdivision of the prefrontal cortex, the anterior cingulate cortex (ACC), in arousal modulation. Pupil size, facial movements, heart rate, and locomotion were used as non-invasive measures of arousal and behavioral state. We designed a closed loop optogenetic system based on machine vision and found that real time inhibition of ACC activity during pupil dilations suppresses ongoing arousal events. In contrast, inhibiting activity in a control cortical region had no effect on arousal. Fiber photometry recordings showed that ACC activity scales with the magnitude of spontaneously occurring pupil dilations/face movements independently of locomotion. Moreover, optogenetic ACC activation increases arousal independently of locomotion. In addition to modulating global arousal, ACC responses to salient sensory stimuli scaled with the size of evoked pupil dilations. Consistent with a role in sustaining saliency-linked arousal events, pupil responses to sensory stimuli were suppressed with ACC inactivation. Finally, our results comparing arousal-related ACC and norepinephrinergic LC neuron activity support a role for the LC in initiation of arousal events which are modulated in real time by the ACC. Collectively, our experiments identify the ACC as a key cortical site for sustaining momentary increases in arousal and provide the foundation for understanding cortical-subcortical dynamics underlying the modulation of arousal states.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".